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Product · Insight Cards

Every card is a problem
you would have found too late.

Margin erosion, promo conflicts, shrinkage spikes, staffing mismatches. Ward catches each one and explains what changed, why, and what to do next. Click any number to see the SQL.

Get a demo →

What changed. Why. What to do next.

Dashboards show what happened, not why or what to do about it. Each insight card surfaces one anomaly, explains the root cause, quantifies the impact, and recommends a next step. They arrive automatically, no login required.

The math behind every number

Forecasts run on ARIMA, Holt-Winters, Bayesian hierarchical, and gradient-boosted residuals. Anomalies use z-score against a 90-day rolling baseline. Each card carries the model card, the MAPE, and the confidence interval. Click the number to see the SQL, source tables, and parameters.

Stockout
Stockout Prediction
+18% CAC increase
Marketing
Pricing
Price Optimization
-3.2pp margin compression
Finance
Promos
Promo Effectiveness
-22% rev/labor hr
Store Ops
Shrinkage
Shrinkage Detection
+6.1% labor cost growth
HR
Demand
Demand Forecasting
23 SKUs at risk
Supply Chain
Customer
Customer Behavior
-2.4pp category margin
Merchandising

The agents behind every card

Define an agent for any job, each on a prebuilt, retail-specific playbook: shrink, store sales, supplier scoring, loss prevention, labor, store ops. Each runs under a written charter: scope, sources, allowed actions, owner, version. Diff two versions side by side. Every edit is logged with name, time, and approver.

app.getward.ai Live demo
Acme Retail @Merchandising: VP Analyst claude-sonnet default
A

Chat

Ask anything. Ward routes to the right agent and returns cited answers.

Why did Store 37 miss target last week?
You · 9:42 AM
Schema Scout · routed to Merchandising Agent

I pulled Store 37’s last 28 days against the chain baseline. Two root causes, both compounding.

SignalFinding
labor_efficiencyRev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak
inventory.freshFresh fill 83%, backroom replenishment lag at 2–4p
promo.liftBOGO crackers cannibalized Brand Y by 28%, net category +6%

Recommend: re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

8 parallel queries 3 sources cited confidence 0.92
Show me how to fix the staffing mismatch.
You · 9:43 AM
Labor Agent · drafting schedule diff
Querying labor_scheduling…
› Ask anything, Ward routes to the right agent. Cmd+K

AI Insights

Agents run against your baselines overnight. These are what they flagged without being asked.

3 flagged 8 queries run 3 sources swept 04:00, acme-retail
Act now labor_efficiency 0.94

Rev/labor-hour −22% vs. cluster, staffing mismatch at 11a–1p peak

Schema Scout · routed to Merchandising Agent Pin Ask Ward Investigate
Review inventory.fresh 0.89

Fresh fill 83%, backroom replenishment lag at 2–4p

Promo Agent · routed via Merchandising Pin Ask Ward Investigate
Watch promo.lift 0.81

BOGO crackers cannibalized Brand Y by 28%, net category +6%

Margin Agent · routed via Finance Pin Ask Ward Investigate
Recommended

Re-baseline Store 37 schedule against true peak, raise replen window to 1p, and review the BOGO before next cycle.

Reporting

Pinned views built from saved data-lake queries. Every number re-derivable from its SQL.

7d13w52w
Revenue vs. forecast
$48.2M
+4.2% WoW
Gross margin %
24.1%
−3.2pp
Fill rate, fresh
83.4%
−4.1pp
Shrink, West region
2.41%
+0.8pp
Revenue vs. forecast 13 weeks actual, 6 weeks forecast, 80% interval
Actual Forecast 80% interval
% of plan 106 94 100 forecast → W−13 W−5 today +6wk
Holt-Winters + weather regressor MAPE 4.1% at 4wk Backtested 24 months Crosses plan in 3 weeks
Forecast error by horizon MAPE, 24-month backtest
1wk 2.1%
2wk 3.0%
4wk 4.1%
8wk 6.3%
13wk 8.9%
Accuracy bar for promo decisions: ≤5% at 4wk
Models in production Every forecast ships a model card
ModelHorizonMAPE
holt_winters4wk4.1%
arima_sarimax13wk8.9%
gbm_demand1wk2.1%
bayes_hiernew store11.4%

Models

Browse, search, and manage data–lake model definitions for your tenant.

Search models…
NameNamespaceVersion
sap_pos_transactionssap1.0
sap_inventory_shrinkagesap1.2
sap_labor_schedulingsap1.0
retail_inventory_weeklyretail1.1
retail_google_ads_dailyretail1.0
retail_meta_ads_dailyretail1.0
retail_ga4_website_dailyretail1.0

Sources

Connect external systems to the data lake.

Search sources…
NameTypeLast sync
sap_pos_transactionsimport2m ago
sap_inventory_shrinkageimport2m ago
sap_labor_schedulingimport14m ago
retail_inventory_weeklyimport1h ago
retail_google_ads_dailyimport1h ago
retail_meta_ads_dailyimport1h ago
retail_ga4_website_dailyimport1h ago

Architecture

Two ways to connect. Federate against your live systems, or ingest into Ward’s data lake. Toggle below.

Your systems · read-only
SAP Retail
Snowflake
BigQuery
Shopify
NCR Voyix
Ward Gateway
TLS 1.3 · AES-256
Querying live · data stays put
Federated answers
SELECT * FROM sap.pos
JOIN retail.inventory_weekly
WHERE store_id = 37
→ insight cards
Ward Data Lake
→ baselined per store
TLS 1.3 in transit AES-256 at rest Read-only credentials SOC 2 Type II underway VPC peering · PrivateLink

Pipelines

Move data from sources into models on a schedule.

NameSourceModelStatusSchedule
sync_sap_pos_transactionssap_pos_transactionspos_transactionsenabledhourly
sync_sap_inventory_shrinkagesap_inventory_shrinkageinventory_shrinkageenableddaily
sync_sap_labor_schedulingsap_labor_schedulinglabor_schedulingenableddaily
sync_retail_inventory_weeklyretail_inventory_weeklyinventory_weeklyenabledweekly
sync_retail_google_ads_dailyretail_google_ads_dailygoogle_ads_dailyenableddaily
sync_retail_meta_ads_dailyretail_meta_ads_dailymeta_ads_dailyenableddaily

Streams

Real-time ingestion pipelines.

0events / min
0streams active
0% delivered
  • pos.txn store_037, basket $42.18
  • inv.move dc_west → store_104
  • labor.clock store_022 shift_start
  • pos.txn store_211, basket $19.04

Policies

Browse and manage Cedar access policies for your tenant.

TLS 1.3 AES-256 Read-only SOC 2 II
Policy IDEffectResources
merch-read-defaultpermitModel::*
finance-read-shrinkagepermitModel::"inventory_shrinkage"
vendor-blockedforbidModel::"labor_*"
region-west-onlypermitTenant::"acme"

Entities

Principals and resources referenced by Cedar policies.

Search UIDs…
Entity UIDTypeTenant
Tenant::"acme-retail"Tenantacme-retail
Model::"sap.pos_transactions"Modelacme-retail
Model::"sap.inventory_shrinkage"Modelacme-retail
Model::"sap.labor_scheduling"Modelacme-retail
Model::"retail.inventory_weekly"Modelacme-retail
Model::"retail.google_ads_daily"Modelacme-retail

Providers

Manage LLM API keys and the model profiles that use them.

API Keys Model Profiles
NameProviderUsed byCreated
anthropic-defaultAnthropic3 profilesApr 22
openai-defaultOpenAI2 profilesApr 22
gemini-defaultGemini1 profileApr 22
ollama-onpremOllama2 profilesApr 22

LLM-agnostic. Bring your own key, route per task. No lock-in.

Settings

Manage your dashboard preferences and account.

Appearance
Theme • Light ° Dark

Light and dark themes are available. Your choice is remembered per browser.

Account
NameAdmin
Emailadmin@acme.io
Tenantacme-retail
Agents on retail playbooks. Live reasoning graph. Full auditability.

Cards by insight type

Ward generates cards across eight core domains, each tuned to a specific operational signal.

Stockout
Stockout Prediction
Know before the shelf empties.
Shrinkage
Shrinkage Detection
Find the leak before it drains you.
Pricing
Price Optimization
Price on elasticity you can measure.
Demand
Demand Forecasting
See demand before it arrives.
Assortment
Assortment Planning
Stock what sells. Cut what doesn't.
Promos
Promo Effectiveness
Know which promos actually work.
Customer
Customer Behavior
Understand the person behind the basket.
Fill Rate
Fill Rate Monitoring
Every empty shelf is a lost sale.
FX
Fixes powered by Insight Cards

Where Insight Cards
does the work.

The insight types that lean on insight cards to deliver value.

Stockout
Stockout Prediction
Know before the shelf empties.
Shrinkage
Shrinkage Detection
Find the leak before it drains you.
Pricing
Price Optimization
Price on elasticity you can measure.
Assortment
Assortment Planning
Stock what sells. Cut what doesn't.
Promos
Promo Effectiveness
Know which promos actually work.
Customer
Customer Behavior
Understand the person behind the basket.
Fill Rate
Fill Rate Monitoring
Every empty shelf is a lost sale.
WHO
Roles that depend on it

Built for the teams
that catch problems first.

VP / Director
VP Merchandising
Merchandising
Director
Director Store Ops
Store Operations
Director / Head
Head of LP
Loss Prevention
Director / Head
Head of E-Com
E-Commerce
C-Suite / VP
CFO
Finance
Director / VP
Head of Procurement
Procurement
KPI
Metrics it moves

The KPIs that change
when this runs.

Fill Rate
On-Shelf Availability
Ward monitors fill rate at the store-category-hour level and triggers insight cards when it drops below your threshold.
ITR
Inventory Turnover Ratio
Ward flags when turn slows below vertical benchmark, signaling overstock.
Shrinkage
Inventory Loss Rate
Ward attributes shrinkage to cause categories and benchmarks each store.
GMROI
Gross Margin Return on Inventory
Ward ranks categories by GMROI to identify inventory tying up capital.
VS
See it. Or compare it.

Operator stories
and the alternatives.

PLAY
Plays in production
How operators run insight cards on Ward today.
PBI
Ward vs Power BI
Why exec dashboards miss what costs you money.

Two ways to start.

Run a fixed-fee pilot on your data, or talk to advisory about a broader engagement.

Get a demo → Talk to advisory

Every card is a problem your team would have found too late.

See what Ward catches on your actual data.

Get a demo → Take the 3-minute assessment

Read-only to start · your LLM keys · SOC 2 Type II underway · or book a call directly

Get started

Find out what your data has been hiding.

Tell us about your operation. We’ll show you the problems Ward catches, and the ones your current tools miss.

Step 1 of 3
What are your goals?
Step 2 of 3
About your operation
Step 3 of 3
Your contact info